The Hartley transform, a real-valued alternative to the complex Fourier transform, is presented as an efficient tool for the analysis and simulation of earthquake accelerograms. This paper is introduced a novel method based on discrete Hartley transform (DHT) and radial basis function (RBF) neural network for generation of artificial earthquake accelerograms from specific target spectrums. Acce...

In this paper, learning algorithms for radial basis function (RBF) networks are discussed. Whereas multilayer perceptrons (MLP) are typically trained with backpropagation algorithms, starting the training procedure with a random initialization of the MLP's parameters, an RBF network may be trained in many different ways. We categorize these RBF training methods into one-, two-, and three-phase ...

The traditional RBF neural network has the problem of slow training speed and low efficiency, this paper puts forward the algorithm of improvement of RBF neural network by AdaBoost algorithm combined with PSO, to expand the application range of the RBF neural network. Firstly, it preprocesses the sample data in training set, and initialize the weights of test data; Secondly, it optimizes and ch...

This study proposes RBF Network hybrid learning with Particle Swarm Optimization (PSO) for better convergence, error rates and classification results. In conventional RBF Network structure, different layers perform different tasks. Hence, it is useful to split the optimization process of hidden layer and output layer of the network accordingly. RBF Network hybrid learning involves two phases. T...

In this paper, a novel controller is proposed for discrete-time nonlinear systems with uncertain output-channel time delays using RBF neural networks and information entropy. The controller is designed by minimizing the quadratic Renyi entropy. The probability density function (PDF) of tracking error is estimated by Parzen windowing technique. The Jacobian information is estimated by an RBF neu...

In this paper we propose the idea of building a new software reliability models using Radial Basis Function (RBF) network. The RBF network is easy to design and the network structure can be represented in a simple mathematical equation. Our goal is to build a generalized model that can be used for software predication [1]. The RBF network was trained with a set of data collected from the testin...

In this paper, the chaotic time series RBF neural network model was designed. A prediction method for underwater acoustic chaotic signal based on RBF neural network is proposed in this paper according to the characteristics of chaotic signal with the short-term prediction. Typical Henon chaotic signal and the actual underwater acoustic chaotic signal are respectively predicted by the RBF neural...

Filtering of signals is of primary importance in signal processing. The design of filters to perform signal estimation is a problem that freeze up in the design of communication systems, control systems, in geophysics & in many other applications & disciplines. Optimum filters are proposed for filtering. In this paper, neural networks have been trained to filter satisfactorily with specified MS...

Air pollution is a challenging issue in some of the large cities in developing countries. Air quality monitoring and interpretation of data are two important factors for air quality management in urban areas. Several methods exist to analyze air quality. Among them, we applied the dynamic neural network (TDNN) and Radial Basis Function (RBF) methods to predict the concentrations of ground-level...